The Elements of Intelligence

IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Life Pub Date : 2023-08-01 DOI:10.1162/artl_a_00410
Christoph Adami
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Abstract

Can machines ever be sentient? Could they perceive and feel things, be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path toward “general intelligence” in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines
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智力的要素
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来源期刊
Artificial Life
Artificial Life 工程技术-计算机:理论方法
CiteScore
4.70
自引率
7.70%
发文量
38
审稿时长
>12 weeks
期刊介绍: Artificial Life, launched in the fall of 1993, has become the unifying forum for the exchange of scientific information on the study of artificial systems that exhibit the behavioral characteristics of natural living systems, through the synthesis or simulation using computational (software), robotic (hardware), and/or physicochemical (wetware) means. Each issue features cutting-edge research on artificial life that advances the state-of-the-art of our knowledge about various aspects of living systems such as: Artificial chemistry and the origins of life Self-assembly, growth, and development Self-replication and self-repair Systems and synthetic biology Perception, cognition, and behavior Embodiment and enactivism Collective behaviors of swarms Evolutionary and ecological dynamics Open-endedness and creativity Social organization and cultural evolution Societal and technological implications Philosophy and aesthetics Applications to biology, medicine, business, education, or entertainment.
期刊最新文献
Complexity, Artificial Life, and Artificial Intelligence. Neurons as Autoencoders. Evolvability in Artificial Development of Large, Complex Structures and the Principle of Terminal Addition. Investigating the Limits of Familiarity-Based Navigation. Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent.
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